A recent trend in performance advertising is a shift from segment targeting to granular targeting. In the early days of online advertising, advertisers who had made the transition from offline media to online media often thought of users as segments with well-established segment rules. For example, a segment might be comprised of “males between 25 and 34 years of age”. Because nearly all offline media channels (e.g. broadcast channels, newspapers, magazines, etc) are only able to measure performance of such offline advertising on the basis of surveys, the statistics that arise from such surveys cover only broad classes or groups of seemingly interchangeable people. In contrast, in the online world, significantly greater granularity of performance measurements are available. With such increased granularity comes the ability to fine-tune an advertising campaign (e.g. for performance with respect to fine-grained targeting), however such fine-tuning presents significant challenges, especially in the area of user modeling techniques that support fine-tuned, highly granular, targeting of users, and more particularly in the area of adaptively finding and targeting “look-alike” users. An advertiser might find (i.e. through measurements in an advertising campaign) that “males of age 28 who live within 10 miles of a metropolis” is the most lucrative segment, and would thus want to target advertising to users who are “males of age 28 who live within 10 miles of a metropolis” and/or who have similar “look-alike” user characteristics. Moreover, the most lucrative segment might shift over time, thus it is desirable that the techniques for finding look-alike users support adaptation of the makeup of the segment as information is cumulatively learned about the segment. It therefore becomes apparent that what is needed are techniques that enable adaptive targeting for finding look-alike users.
Other automated features and advantages of the present invention will be apparent from the accompanying drawings and from the detailed description that follows below.